Citation:

journal.pone.0200638

Name: VECTRI simulations of malaria incidence in the highlands of Kenya, accounting for climate, model and initial condition uncertainty using a genetic algorithm framework
Published: 2018-05-23
License: https://creativecommons.org/licenses/by/3.0/

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Published: 2018-05-23 • DOI registered: 2018-06-27

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Abstract:

The output is simulated cases per 1000 population for a highland location in the Kenyan highlands. Each file represents a single ensemble member integration of the model in the final generation of the genetic algorithm calibration method outlined in the paper. The global meta data gives the set of parameters used to define this run, as well as the exact version of the model code used (ref. http://www.gitlab.com/tompkins/vectri).

Coverage:

Latitude: 1.000000 * Longitude: 38.000000

Event(s):

Kenya * Latitude: 1.000000 * Longitude: 38.000000 * Location: Africa, Kenya

Parameter(s):

#	Name	Short Name	Unit	Principal Investigator
1	File content	Content		Tompkins, Adrian Mark
2	File name	File name		Tompkins, Adrian Mark
3	File format	File format		Tompkins, Adrian Mark
4	File size	File size	kByte	Tompkins, Adrian Mark
5	Uniform resource locator/link to file	URL file		Tompkins, Adrian Mark

License:

Creative Commons Attribution 3.0 Unported (CC-BY-3.0)

Size:

15 data points

Data

Download dataset as tab-delimited text — use the following character encoding:

1 Content	2 File name	3 File format	4 File size [kByte]	5 URL file
model, climate and initial conditions are all calibrated	vectri_allpars	TAR	1001	vectri_allpars.tar
only model parameters are calibrated	vectri_modelonly	TAR	1001	vectri_modelonly.tar
only climate uncertainty parameters are calibrated	vectri_climateonly	TAR	1001	vectri_climateonly.tar